在社群大數據的影響下，消費者大量的發表評論於社群網路上。而為了得知消費者的意見傾向，情感分析被大量的應用於文本資料的分析上。然而在過去的文獻中較少應用深度學習於中文評論上，因此本研究檢驗深度學習應用於情感分析的效果。 本研究開發網路爬蟲程式蒐集Google Play上總計196,651條評論，以深度學習、貝氏演算法、支援向量機，三種方法進行情感分析，並比較效果與正確率。 實驗結果顯示，貝氏演算法的正確率為74.12%、支援向量機得到76.46%、深度學習預測模型的正確率為94%。從而證明深度學習在情感分析上的預測效果最為出色。 本論文的研究貢獻為 (1)本研究提出一套使用深度學習於中文手機應用程式消費者評論情感分析方法，實驗結果證實本研究所提出的深度學習方法能有效提升消費者評論情感分析正確率。(2)本研究透過文本資料分析，建構出適用於手機應用程式領域的正負向意見詞典 Influenced by Big Data, there are a large number of customers shared their product reviews on social media. Therefore, many researchers implement sentiment analysis technique on consumer reviews to understand the opinion tendency. However, there are few research about implement deep learning method on Chinese customer reviews. It is therefore the intent of the present study to examine the effect of sentiment analysis with deep learning method. The study used web mining technique collected 196,651 reviews on Google Play. In addition, we use deep learning, Naïve Bayes, Support vector machine methods for sentiment analysis and compared the result. The present study display the accuracy of the Naïve Bayes is 74.12%, the accuracy of Support vector machine is 76.46%, and the accuracy of deep learning is 94%. Our finding confirm that sentiment analysis with deep learning is outstanding. There are three contributions in present finding. First, the present study confirm sentiment analysis with deep learning on Chinese cell phone application customers reviews may improve the accuracy of prediction. Second, the present study create a sentiment dictionary of cell phone application. Third, the study compared the result of average sampling data and non-average sampling data. We found that deep learning method with non-average sampling data reached the better performance.
